The transition to Data Processing Unit (DPU)-centric architectures has become the de-facto standard in modern cloud networks, enabling infrastructure offload and improved host resource utilization. However, the fixed hardware limits of DPUs increasingly fail to keep pace with the rapid growth of host compute density and network-intensive workloads. As a result, when DPU resources are saturated, host compute capacity often remains underutilized due to insufficient network provisioning.

The rapid expansion of cloud applications has led to unprecedented increases in network traffic volume, diversity, and complexity. As Cloud Service Providers (CSPs) adopt decentralized, geographically distributed data centers, effective traffic management across these environments has become critical. Distributed Rate Limiting (DRL) has emerged as an essential tool to manage the complex traffic dynamics of decentralized networks, yet traditional centralized rate limiting methods fall short, facing limitations in scalability, adaptability to bursty traffic, and efficiency. This paper presents C3PDAR (Cloud Control with Constant Probabilities and Dynamic Adjustment Range), a novel DRL algorithm tailored for decentralized cloud infrastructures. C3PDAR introduces three key innovations: (1) CPS-BPS DualPoint Rate Limiting and Parent-Child Token Bucket mechanisms, which effectively mitigate burst traffic and short-lived connections while improving bandwidth fairness and inter-tenant isolation; (2) A vSwitch-CGW Cascade Rate Limiting architecture, which reduces CPU overhead in CGW clusters and accelerates convergence by 42%–78%; (3) Virtual Extensible Local Area Network (VXLAN) Padding scheme, which embeds rate-limiting information in existing traffic instead of transmitting new data packets, reducing the communication overhead of the C3PDAR algorithm by over 40%. By integrating these advancements, C3PDAR delivers a scalable, robust solution that outperforms traditional DRL approaches in performance, fault tolerance, and resource efficiency. C3PDAR uniquely empowers CSPs to manage complex, high-volume traffic dynamics in decentralized cloud environments, offering both theoretical insights and practical optimizations for next-generation network control.

As cloud networks continue to evolve, network traffic has experienced an exponential increase. The network architecture is progressively adopting a distributed structure to address this challenge. This architecture extensively utilizes technologies like gateway clusters and Equal-Cost Multi-Path (ECMP) routing, enabling traffic from individual tenants to be routed through multiple pathways. As a result, distributed rate limiting (DRL) has emerged as an essential aspect. Nonetheless, the shift from centralized to DRL has encountered obstacles, with the associated algorithms grappling with simplicity, precision, and applicability issues. Consequently, our research seeks to reconceptualize the issue of DRL from a theoretical standpoint to discover a more holistic and efficacious solution.

Apsara vSwitch (AVS) is a per-host deployed forwarding component for instance network connectivity in the Alibaba Cloud. To meet the growing performance demands, we accelerated AVS by adopting the most widely used "Sep-path" offloading architecture, which introduces a separate hardware data path to speed up popular traffic. However, the deployment results prove that it is difficult to bridge the gap in performance and programming flexibility of the software and hardware data paths, resulting in unpredictable performance and low iteration velocity.

The vSwitch, as a critical component for Virtual Machine (VM) network connectivity in cloud environments, has prompted increasing attention towards its forwarding performance. While software optimization schemes have limitations in meeting the expanding network capacity demands [11, 12, 15, 17, 18], hardware offloading architectures leveraging SoC, FPGA, and ASIC have been proposed to transfer the match-action workload [1, 3, 6, 7, 13, 16], addressing the growing need for network capacity.

Cloud computing has witnessed tremendous growth, prompting enterprises to migrate to the cloud for reliable and on-demand computing. Within a single Virtual Private Cloud (VPC), the number of instances (such as VMs, bare metals, and containers) has reached millions, posing challenges related to supporting millions of instances with network location decoupling from the underlying hardware, high elastic performance, and high reliability. However, academic studies have primarily focused on specific issues like high-speed data plane and virtualized routing infrastructure, while existing industrial network technologies fail to adequately address these challenges.

Conventional video coding schemes employ a hybrid motion prediction / residual transform coding paradigm, which only exploits redundancy in individual pairs of video frames for compression gain. However, rigid geometric structures in 3D space—e.g., a building in a scene’s background—persist across time in a large frame group. Thus if one can extract and encode the geometric structure, then redundancy across the entire frame group can be removed in one shot. In this paper, we extract a best-fitting "semi-regular" geometric structure from a target spatial region in a frame group, which is encoded separately as a unified signal predictor for these frames. By semi-regular, we mean its geometry is simple enough that its shape parameters can be encoded cheaply. This semi-regular structure kernel approximates the 3D shape of an object in the video, on which we project pixels from the frame group to a carefully spaced 2D grid overlaid on the kernel. We encode the projected pixels as an intra-frame using HEVC. The decoded pixels are then back-projected to each frame as the predictor, and the resulting prediction residuals are transform-coded. Experimental results show that employing a semi-regular geometric kernel—a folded 2D plane in our realization—improves coding performance over native HEVC implementation and our previous regular kernel based scheme.